February 4, 2022
Digital Health Platforms: Considerations for Study Design and the IRB
Clinical Trials & Research
September 15, 2022
Risk-based monitoring (RBM) is the practice of identifying and tracking risks within your clinical trial or research study, which can be used to inform your decision to take corrective action. Conventional study designs require tedious review of all source data to acquire actionable information. Digital study designs, however, yield more complete, actionable data that can be utilized in real-time for RBM. RBM is the clinical trial version of remote patient monitoring, which is emerging in the healthcare setting to track patients outside the clinic or hospital.
Monitoring your risks alone isn’t sufficient for promoting clinical trial success—you’ll also want to take corrective action, intervening to reduce or resolve your risks. For example, you might be concerned about medication noncompliance in your study. Implementing an electronic patient drug diary will allow your team to remotely monitor the participant for risk of noncompliance. However, your action plan—intervening and engaging with the participant—is what will improve the quality and likelihood of success of your research study.
We’ve outlined some of the most effective tips below to help you and your team implement an RBM action plan in your next digital clinical research study.
Which of your study’s assessments are critical to the study endpoints? With technology, it is tempting to create automated nudges to ensure participants complete all assessments. Unfortunately, we all become numb to too many messages that require our attention. Your RBM and resulting intervention should weigh how critical an assessment is to your study endpoints against the risk of notification fatigue. Those assessments deemed critical should then be prioritized in any automated outreach, perhaps even at the risk of losing exploratory data.
Consider a study collecting sleep data and occupational health information. If sleep data is integral to your study endpoints, adherence notifications for these data should be prioritized over those for an incomplete occupational health survey that isn’t critical for the primary study objective. For participants with an incomplete occupational health survey, first check to ensure their wearable device is syncing and providing sleep data.
If it isn’t syncing, send a message that asks them to check on their device, charging if necessary. Once sleep data is being received, then proceed with the occupational health survey reminder. While participants are less likely to engage with subsequent nudges, organizing your notifications from most to least important will help ensure critical data is prioritized accordingly, reducing the risk of missing data that impact study endpoints.
A major benefit of digital clinical trials is the efficient utilization of resources, including staff time. Using technology, you can collect daily surveys and send automated reminders for incomplete tasks without needing to see the participant. Automation enables scalability and allows staff time to be redirected to high-value tasks, including engaging participants that might require assistance or extra attention. Automation should not be mutually exclusive with a personal touch, though. Use personalization tokens and other features to help build a relationship with your participants even without high-touch interactions.
If you’re sending a reminder to reconnect their Fitbit account, why not make the content of the automated message warmer and more inviting? Dynamically add their name to the message and explain how beneficial their contributions are to the study. Include a study team signature and contact information in the message so they remember there are people behind their participation in the study and know how to contact you should they require assistance. You might not meet your participants in-person, but they should still feel connected to the study’s outcomes and the team behind it.
Digital study designs allow you to collect more complete, continuous, real-world data. From the wealth of accessible information you can collect, consider which data points might be able to flag health risks for participants. Then, configure study team notifications or data visualizations that facilitate awareness of participant safety concerns in a timely manner.
Perhaps you collect a daily mood survey in your digital clinical research study and your RBM action plan calls for intervention after a pattern of concerning behavior. Setting up coordinator notifications for when survey results suggest suicide ideation can help protect the participant. Similarly, if you are collecting health data like electrocardiograms or blood pressure, your team can identify thresholds that might constitute a health risk and automatically trigger notifications to be sent to the team when a participant demonstrates concerning results.
Setting expectations is a useful strategy for mitigating noncompliance in a burdensome protocol. While your consent form likely outlines the assessments and data that will be collected throughout your study, consider providing your participants with periodic information about what to expect next. Setting the participant’s expectations can ultimately improve their engagement with your study.
Similarly, be sure to consider rewards for participants. It needn’t be monetary—even a simple thank you at the end of a survey or a notification stating they completed all surveys this week can be impactful. These rewards help to reinforce the expectations necessary for your study to be successful.
For a high-intensity study with many assessments delivered in quick succession, consider sending the participant a notification at the beginning of the week outlining what is ahead. At the end of the week, congratulate and thank them for the assessments they completed and stress the importance of their (continued) contribution.
Acknowledge that your action plan may need to adapt as your trial progresses based on real-world data collected through your protocol. A great way to improve your action plan is to implement A/B testing—try out different interventions on randomized cohorts of participants and evaluate what works best.
Consider randomizing participants into different cohorts and sending different assessment reminders and congratulatory notifications to those in cohort A vs B. Determine which one works better based on predetermined metrics and adopt that one moving forward! Through A/B testing, you can refine your RBM action plan overtime and find what improves your study’s quality.
If your research studies the impact of exercise on health outcomes, you might wish to understand what type of feedback your participants respond best to in order to encourage that they complete an exercise routine.
With a large enough sample size, you can randomly assign participants to two cohorts: one cohort could receive reminders to exercise at 6pm on days when they haven’t yet, and the other cohort could get positive feedback on days when they do exercise. Then, after a pilot period of time, you can determine if participants that receive reminders or participants that receive positive feedback demonstrate a higher adherence to the exercise routine.